arXiv AI
Verify Before You Fix: Agentic Execution Grounding for Trustworthy Cross-Language Code Analysis
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Program Semantic Inequivalence Game with Large Language Models
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv AI
Jun 24
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
arXiv AI
Aug 24
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
arXiv AI
Jul 8
SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models
arXiv:2512. 18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both traditional web security and AI/ML-specific defenses in a format suitable for instruction tuning.
arXiv AI
Aug 5
AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
arXiv:2601. 19138v2 Announce Type: replace-cross Abstract: Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, delaying feedback and increasing remediation costs.
arXiv AI
Sep 7
Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points
The paper surveys 87 influential studies on machine‑learning‑based automated vulnerability detection (ML4AVD), categorizing them by problem formulation, input and detection granularity, target languages, evaluation metrics, datasets, and detection approaches. It identifies twelve self‑reinforcing pain points—such as overreliance on binary classification of C/C++ function‑level vulnerabilities, limited language coverage, and intertwined datasets, baselines, and metrics—that trap the field in a narrow, artificial problem space. The authors propose concrete recommendations to break these feedback loops and evaluate a recent high‑profile effort, AIxCC, against these guidelines, reflecting on ML4AVD’s relevance amid the rise of agentic AI.